Examples
In many cases, learning a new API is best done by following along with the examples. There are two ways to use gpCAM, through the Autonomous Experimenter or the gpOptimizer functionalities. The Autonomous Experimenter is a great way to get started and contains most customization options. For full control, use the gpOptimizer.
For even more examples please visit the documentation.
For the data some of the tests need, please check out the GitHub pages.
An Autonomous Experimenter basic minimal example.
gpCAM Autonomous Experimenter advanced-user example.
A basic GPOptimizer Example.
A bunch of different acquisition functions.
fvGPOptimizer for multi-tasking; here we also show deep kernel learning.
Large-scale GPs, Bayesian Optimization, and Autonomous Experimentation.
HGDL for constrained optimization.
Bayesian Optimization and Autonomous Experimentation on non-Euclidean input spaces.
A quick and compact solution to BO problems.
Solve a classification problem with gpCAM.
Supplemental Material: A Unifying Perspective on Non-Stationary Kernels for Deeper GPs.